A hypothesis regarding the poor blue constancy of CIELAB

Moroney, Nathan

doi:10.1002/col.10180

Cited by 16 publications

(6 citation statements)

References 26 publications

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“…In the DIN99d color space the blue and yellow regions remain almost unchanged, but for the CIELAB color space the blue region changed a little. This difference between the representations in the two color spaces may be due to the reported non-uniformity of the CIELAB space in the blue region [43]. The analysis of the graphs also shows that there are colors for the anomalous observers that the normal observer cannot perceive, that is, colors with no perceptual correspondence.…”

Section: Resultsmentioning

confidence: 95%

Number of discernible colors for color-deficient observers estimated from the MacAdam limits

et al. 2010

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We estimated the number of colors perceived by color normal and color-deficient observers when looking at the theoretic limits of object-color stimuli. These limits, the optimal color stimuli, were computed for a color normal observer and CIE standard illuminant D65, and the resultant colors were expressed in the CIELAB and DIN99d color spaces. The corresponding color volumes for abnormal color vision were computed using models simulating for normal trichromatic observers the appearance for dichromats and anomalous trichomats. The number of colors perceived in each case was then computed from the color volumes enclosed by the optimal colors also known as MacAdam limits. It was estimated that dichromats perceive less than 1% of the colors perceived by normal trichromats and that anomalous trichromats perceive 50%-60% for anomalies in the medium-wavelength-sensitive and 60%-70% for anomalies in the long-wavelength-sensitive cones. Complementary estimates obtained similarly for the spectral locus of monochromatic stimuli suggest less impairment for color-deficient observers, a fact that is explained by the two-dimensional nature of the locus.

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Section: Resultsmentioning

confidence: 95%

Number of discernible colors for color-deficient observers estimated from the MacAdam limits

et al. 2010

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“…One model which includes higher non-linear luminance and chromatic adaption effects, like the Hunt-Effect (Hunt, 1977), which are intentionally excluded in IPT, was latest derived as CAM16 (Li et al, 2017), which solved computational failures during image processing of the previous CIECAM02 colorappearance model by CIE TC 8-01 (Moroney et al, 2002 We selected IPT, based on the advantages that it originated from a newer dataset compared to CIELAB, improved hue linearity, especially in the bluish region (Moroney, 2003), and a simple formula approach. But since perceptual adaption effects are not included, we chose also CAM16 as a color appearance model, which gives us the possibility to compare the performance of both.…”

Section: Opponent-color Theory and Perceptual Attributesmentioning

confidence: 99%

“…We selected IPT, based on the advantages that it originated from a newer dataset compared to CIELAB, improved hue linearity, especially in the bluish region (Moroney, 2003 ), and a simple formula approach. But since perceptual adaption effects are not included, we chose also CAM16 as a color appearance model, which gives us the possibility to compare the performance of both.…”

Section: Opponent-color Theory and Perceptual Attributesmentioning

confidence: 99%

Evidence for human-centric in-vehicle lighting: Part 2—Modeling illumination based on color-opponents

2022

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Illumination preference models are usually defined in a static scenery, rating common-colored objects by a single scale or semantic differentials. Recently, it was reported that two to three illumination characteristics are necessary to define a high correlation in a bright office-like environment. However, white-light illumination preferences for vehicle-occupants in a dynamic semi- to full automated modern driving context are missing. Here we conducted a global free access online survey using VR engines to create 360° sRGB static in-vehicle sceneries. A total of 164 participants from China and Europe answered three levels in our self-hosted questionnaire by using mobile access devices. First, the absolute perceptional difference should be defined by a variation of CCT for 3,000, 4,500, and 6,000 K or combinations, and light distribution, either in a spot- or spatial way. Second, psychological light attributes should be associated with the same illumination and scenery settings. Finally, we created four driving environments with varying external levels of interest and time of the day. We identified three key results: (1) Four illumination groups could be classified by applying nMDS. (2) Combinations of mixed CCTs and spatial light distributions outperformed compared single light settings (p < 0.05), suggesting that also during daylight conditions artificial light supplements are necessary. (3) By an image transformation in the IPT and CAM16 color appearance space, comparing external and in-vehicle scenery, individual illumination working areas for each driving scenery could be identified, especially in the dimension of chroma-, partially following the Hunt-Effect, and lightness contrast, which synchronizes the internal and external brightness level. We classified our results as a starting point, which we intend to prove in a follow-up-controlled laboratory study with real object arrangements. Also, by applying novel methods to display high fidelity 360° rendered images on mobile access devices, our approach can be used in the future interdisciplinary research since high computational mobile devices with advanced equipped sensory systems are the new standard of our daily life.

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“…While previous studies 23,25 used theoretical reflectance functions and standard illuminants (white light), here boundaries were determined using realistic reflectance functions and theoretical light sources (white and non‐white light). Differences in the blue‐purple (positive a * – negative b *) region support the poor blue constancy in CIELAB 35 . The poor blue constancy might be due to the relatively low reflectance of blue and purple objects, both natural and manufactured.…”

Section: Resultsmentioning

confidence: 77%

CIELAB color space boundaries under theoretical spectra and 99 test color samples

Durmus

2020

Color Research & Application

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A color space is a three‐dimensional representation of all the possible color percepts. The CIE 1976 L*a*b* is one of the most widely used object color spaces. In CIELAB, lightness L* is limited between 0 and 100, while a* and b* coordinates have no fixed boundaries. The outer boundaries of CIELAB have been previously calculated using theoretical object spectral reflectance functions and the CIE 1931 and 1964 observers under the CIE standard illuminants D50 and D65. However, natural and manufactured objects reflect light smoothly as opposed to theoretical spectral reflectance functions. Here, data generated from a linear optimization method are analyzed to re‐evaluate the outer boundaries of the CIELAB. The color appearance of 99 test color samples under theoretical test spectra has been calculated in the CIELAB using CIE 1931 standard observer. The lightness L* boundary ranged between 6 and 97, redness‐greenness a* boundary ranged between −199 and 270, and yellowness‐blueness b* boundary ranged between −74 and 161. The boundary in the direction of positive b* (yellowness) was close to the previous findings. While the positive a* (redness) boundary exceeded previously known limits, the negative a* (greenness) and b* (blueness) boundaries were lower than the previously calculated CIELAB boundaries. The boundaries found here are dependent on the color samples used here and the spectral shape of the test light sources. Irregular spectral shapes and more saturated color samples can result in extended boundaries at the expense of computational time and power.

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A hypothesis regarding the poor blue constancy of CIELAB

Cited by 16 publications

References 26 publications

Number of discernible colors for color-deficient observers estimated from the MacAdam limits

Number of discernible colors for color-deficient observers estimated from the MacAdam limits

Evidence for human-centric in-vehicle lighting: Part 2—Modeling illumination based on color-opponents

CIELAB color space boundaries under theoretical spectra and 99 test color samples

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